Considering the noise components in automatic monitoring data and the complex nonlinear relationship between dam deformation and environmental factors, a dam deformation monitoring model based on singular spectrum analysis(SSA) and support vector machine(SVM) optimized by particle swarm optimization(PSO) was proposed. SSA was used to decompose the measured deformation, and its intrinsic trend and periodic components were extracted and reconstructed. The complex nonlinear relationship between reconstruction deformation and environmental factors was then mined based on SVM optimized by PSO. The case validation results show that the model has good fitting and prediction accuracy and it can effectively mine the data characteristics inherent in the measured deformation, reduce the influence of noise components on the modeling accuracy, and has certain engineering application value.